How does this skill improve AI explainability?

It creates traceable reasoning chains from perception to action, allowing agents to justify their decisions based on specific beliefs, desires, and underlying RDF context.

What is BDI architecture?

BDI (Belief-Desire-Intention) is a software model for programming intelligent agents by mimicking human-like cognitive processes to handle complex reasoning.

Can this skill work with any LLM?

Yes, while designed as a Claude Code Skill, its logic-augmented generation (LAG) patterns can be applied to enhance any Large Language Model with formal cognitive structures.

What is the T2B2T paradigm?

Triples-to-Beliefs-to-Triples is a process that converts RDF knowledge graph data into internal mental states and then projects resulting intentions back into actionable RDF data.

Is it compatible with existing agent frameworks?

Yes, it includes patterns for integration with established BDI platforms such as JADE, JADEX, and SEMAS.

BDI Mental State Modeling

Name: BDI Mental State Modeling
Author: muratcankoylan

bymuratcankoylan

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5,498

Data Science & ML

Transforms external RDF context into agent mental states to enable cognitive reasoning, explainability, and semantic interoperability in multi-agent systems.

About

The BDI Mental State Modeling skill provides a formal framework for implementing Belief-Desire-Intention architectures within AI agents. It leverages semantic web technologies like RDF and OWL to bridge the gap between raw data and cognitive reasoning. By modeling mental states as distinct entities—beliefs (what is known), desires (what is wanted), and intentions (what is planned)—this skill enables developers to build highly deliberative agents with traceable reasoning chains. It is particularly useful for building multi-agent systems (MAS), logic-augmented generation (LAG) pipelines, and neuro-symbolic AI applications where explainability and goal-directed behavior are critical.

Key Features

Triples-to-Beliefs-to-Triples (T2B2T) bidirectional context flow
Logic Augmented Generation (LAG) for constraining LLM outputs
Traceable reasoning chains for enhanced AI explainability
Temporal dimension tracking for mental state evolution and validity
Formal BDI ontology patterns for beliefs, desires, and intentions
5,498 GitHub stars

Use Cases

Building sophisticated cognitive agents for complex decision-making tasks
Coordinating mental states across decentralized multi-agent architectures
Implementing explainable AI systems that justify actions via belief-desire links

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add muratcankoylan/agent-skills-for-context-engineering bdi-mental-states

For use in Claude.ai and ChatGPT

Download Skill

GitHub